ArticleJournal of chemical theory and computation2024
Hyperparameter Optimization for Atomic Cluster Expansion Potentials.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.Nature communications · 2025Article
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb
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